Data analysis device and data recognition device

ABSTRACT

The data analysis device  100  includes: difference calculation means (S 2 ) for, with respect to an image, carrying out a calculation of calculating a difference between intensity values at an arbitrary point in the image and a point located in the vicinity of the arbitrary point in a first direction as a first intensity difference of the arbitrary point and of calculating a difference between intensity values at the arbitrary point and a point located in the vicinity of the arbitrary point in a second direction different from the first direction as a second intensity difference of the arbitrary point, the difference calculation means carrying out the calculation with respect to each of a plurality of points in the image; and frequency distribution generation means (S 3  to S 5 ) for quantizing a vector comprising the first intensity difference and the second intensity difference obtained by the difference calculation means for each of the plurality of points in the image into a single region of a plurality of regions (regions represented by the index numbers 0 to 49 of the quantization table in FIG.  4 ) divided by a predetermined region division method and generating a frequency distribution of the plurality of regions by using the number of vectors quantized for each of the plurality of regions as frequency of the corresponding region.

TECHNICAL FIELD

This invention relates to a data analysis device for analyzing imagedata. This invention further relates to a data recognition device usingsuch a data analysis device and, in particular, relates to a datarecognition device effective as a recognition device for an image suchas a face.

BACKGROUND ART

Recently, attention has been paid to individualidentification/authentication systems that make use of biometricalfeatures of individuals such as faces, voices, fingerprints, and irispatterns. Among them, face recognition is considered the most naturaland most effective method for identifying an individual because it islike what a human being does and it does not require use of particularfacilities. In the face recognition, extraction of features of anindividual face is a key for construction of a more effective system.Although many feature extraction techniques have been proposed, thesetechniques are fairly complicated and therefore it is difficult to applythem to real-time face recognition.

Recently, there has been proposed a very simple and highly reliable facerecognition method on the basis of a vector quantization (VQ) algorithm(see Non-Patent Document 1 below).

Similar data recognition devices are disclosed also in Patent Document 1and Patent Document 2 below.

Non-Patent Document 1:

K. Kotani, C. Qiu, and T. Ohmi, “Face Recognition Using VectorQuantization Histogram Method”, Proc. 2002 Int. Conf. on ImageProcessing, Vol. II of III, pp. II-105-II-108, 2002

Patent Document 1:

Japanese Unexamined Patent Application Publication (JP-A) No.2000-101437

Patent Document 2:

Japanese Unexamined Patent Application Publication (JP-A) No.2002-203241

In the foregoing face recognition method, a histogram generated fromusage frequency of each of code vectors obtained by VQ processing of aface image is used as a very effective individual feature extractiontechnique. By applying proper filtering and VQ processing to a faceimage, it is possible to extract useful features for face recognition.The result of a test using the AT&T face database showed a recognitionrate of 95.6%. When a 1.1 GHz personal computer is used, a processingtime for one image is 194 msec. The VQ histogram method is much simplerand faster than previous face recognition methods but is still notsufficient for application to high-speed data recognition such as videorate (standard video is 30 frames per second and the video raterepresents an image recognition speed of about 33 msec corresponding toone frame) recognition.

DISCLOSURE OF THE INVENTION

Therefore, it is an object of this invention is to provide a dataanalysis device that achieves high processing speed and a datarecognition device using such a data analysis device, thereby enablinghigh-speed data recognition.

Data analysis devices and data recognition devices according to thisinvention are as follows.

(1) A data analysis device characterized by comprising

difference calculation means for, with respect to an image, carrying outa calculation of calculating a difference between intensity values at anarbitrary point in said image and a point located in the vicinity ofsaid arbitrary point in a first direction as a first intensitydifference of said arbitrary point and of calculating a differencebetween intensity values at said arbitrary point and a point located inthe vicinity of said arbitrary point in a second direction differentfrom said first direction as a second intensity difference of saidarbitrary point, said difference calculation means carrying out saidcalculation with respect to each of a plurality of points in said image,and

frequency distribution generation means for allocating a vectorcomprising said first intensity difference and said second intensitydifference obtained by said difference calculation means for each of theplurality of points in said image to a single region of a plurality ofregions divided by a predetermined region division method and generatinga frequency distribution of said plurality of regions by using thenumber of vectors allocated to each of said plurality of regions asfrequency of the corresponding region.

(2) A data analysis device according to the above-described item (1),characterized by producing feature data by extracting a frequencydistribution of at least a part of said plurality of regions from thefrequency distribution of said plurality of regions generated by saidfrequency distribution generation means.

(3) A data analysis device according to the above-described item (1) or(2), characterized by applying filtering to said image before saiddifference calculation means carries out said calculation with respectto said image.

(4) A data recognition device characterized by comprising

frequency distribution storage means for storing information of one ormore frequency distributions with respect to at least one or moreimages,

difference calculation means for, with respect to an image, carrying outa calcultion of calculating a difference between intensity values at anarbitrary point in said image and a point located in the vicinity ofsaid arbitrary point in a first direction as a first intensitydifference of said arbitrary point and of calculating a differencebetween intensity values at said arbitrary point and a point located inthe vicinity of said arbitrary point in a second direction differentfrom said first direction as a second intensity difference of saidarbitrary point, said difference calculation means carrying out saidcalculation with respect to each of a plurality of points in said image,

frequency distribution generation means for allocating a vectorcomprising said first intensity difference and said second intensitydifference obtained by said difference calculation means for each of theplurality of points in said image to a single region of a plurality ofregions divided by a predetermined region division method and generatinga frequency distribution of said plurality of regions by using thenumber of vectors allocated to each of said plurality of regions asfrequency of the corresponding region, and

comparison means for comparing between one or more frequencydistributions about said image generated by said frequency distributiongeneration means and said one or more frequency distributions in saidfrequency distribution storage means.

(5) A data recognition device according to the above-described item (4),characterized in that said comparison means compares between said one ormore frequency distributions about said image generated by saidfrequency distribution generation means and said frequency distributionsin said frequency distribution storage means and selects the frequencydistribution specified by a predetermined comparison function from therespective frequency distributions stored in said frequency distributionstorage means.

(6) A data recognition device according to the above-described item (5),characterized in that said one or more frequency distributions aboutsaid image generated by said frequency distribution generation means areone or more frequency distributions of a part of said plurality ofregions extracted from the frequency distribution of said plurality ofregions generated by said frequency distribution generation means.

(7) A data recognition device according to any of the above-describeditems (4) to (6), characterized by applying filtering to said imagebefore said difference calculation means performs said calculation withrespect to said image.

(8) A data recognition device characterized by comprising

filter means for applying filtering to input image data,

difference calculation means for, with respect to the image data appliedwith the filtering by said filter means, carrying out a calcultion ofcalculating an intensity difference dIx of an arbitrary point in anx-direction in an image as a difference between an intensity value atsaid arbitrary point and an intensity value at a point on a right orleft side of said arbitrary point and of calculating an intensitydifference dIy of said arbitrary point in a y-direction as a differencebetween the intensity value at said arbitrary point and an intensityvalue at a point on a lower or upper side of said arbitrary point, saiddifference calculation means carrying out said calculation with respectto each of a plurality of points in said image,

frequency distribution generation means for allocating a vectorcomprising said intensity difference in the x-direction and saidintensity difference in the y-direction obtained by said differencecalculation means for each of the plurality of points in said image to asingle region of a plurality of regions divided by a predeterminedregion division method and generating a frequency distribution of saidplurality of regions by using the number of vectors allocated to each ofsaid plurality of regions as frequency of the corresponding region,

frequency distribution storage means for storing information of at leastone or more frequency distributions, and

comparison means for comparing between the frequency distribution aboutsaid input image data generated by said frequency distributiongeneration means and said frequency distributions in said frequencydistribution storage means and selecting the frequency distributionspecified by a predetermined comparison function from the respectivefrequency distributions stored in said frequency distribution storagemeans.

(9) A data recognition device characterized by comprising

a plurality of filter means for applying a plurality of filteringprocesses to input image data,

difference calculation means for, with respect to the image data appliedwith the filtering processes by said plurality of filter means, carryingout a calcultion of calculating an intensity difference dIx of anarbitrary point in an x-direction in an image as a difference between anintensity value at said arbitrary point and an intensity value at apoint on a right or left side of said arbitrary point and of calculatingan intensity difference dIy of said arbitrary point in a y-direction asa difference between the intensity value at said arbitrary point and anintensity value at a point on a lower or upper side of said arbitrarypoint, said difference calculation means carrying out said calculationwith respect to each of a plurality of points in said image,

frequency distribution generation means for allocating a vectorcomprising said intensity difference in the x-direction and saidintensity difference in the y-direction obtained by said differencecalculation means for each of the plurality of points in said image to asingle region of a plurality of regions divided by a predeterminedregion division method and generating a frequency distribution of saidplurality of regions by using the number of vectors allocated to each ofsaid plurality of regions as frequency of the corresponding region,

frequency distribution storage means for storing, in a plurality ofsets, information of the frequency distributions of said plurality ofregions, and

comparison means for comparing between the frequency distribution aboutsaid input image data generated by said frequency distributiongeneration means and the respective sets of the frequency distributionsin said frequency distribution storage means and selecting one set ofthe frequency distributions specified by a predetermined comparisonfunction from the respective sets of the frequency distributions storedin said frequency distribution storage means.

(10) A data recognition device according to any of the above-describeditems 5, 6, 8 and 9, characterized by comprising frequency distributionregistration means for registering the frequency distribution generatedby said frequency distribution generation means into said frequencydistribution storage means when, as a result of the comparison by saidcomparison means, the frequency distribution to be selected by saidpredetermined comparison function does not exist in said frequencydistribution storage means.

According to this invention, there are obtained a data analysis devicethat achieves high processing speed and a data recognition device usingsuch a data analysis device, thereby enabling high-speed datarecognition or instantaneous data recognition.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart showing recognition processing steps of anadjacent pixel intensity difference quantization (APIDQ) histogrammethod used in a data recognition device according to an embodiment ofthis invention.

FIG. 2 is a diagram used in explaining calculation of adjacent pixelintensity differences according to the embodiment of this invention.

FIG. 3 is a diagram used in explaining the operation of the embodimentof this invention, wherein a typical example of (dIx, dIy) vectordistribution is shown.

FIG. 4 is a diagram used in explaining the operation of the embodimentof this invention, wherein the r-θ plane and a quantization table areshown.

FIG. 5 is a diagram showing samples of typical face images of the AT&Tdatabase used in the embodiment of this invention.

FIG. 6 is a diagram used in explaining the operation of the embodimentof this invention, wherein typical examples of histograms are shown.

FIG. 7 is a diagram used in explaining the effect of the embodiment ofthis invention, wherein the recognition success rate is shown as afunction of filter size.

FIG. 8 is a diagram used in explaining the effect of the embodiment ofthis invention, wherein recognition results obtained by the use ofmultiple filters are shown.

BEST MODE FOR CARRYING OUT THE INVENTION

Now, an embodiment of this invention will be described with reference tothe drawings.

The present inventors have developed a new, very simple, and effectivehigh-speed data recognition method called an adjacent pixel intensitydifference quantization (APIDQ) histogram method, which enableshigh-speed data recognition (e.g. video-rate face recognition).

FIG. 1 shows processing steps of the adjacent pixel intensity differencequantization (APIDQ) histogram method used in a data recognition deviceaccording to the embodiment of this invention.

At first, referring to FIG. 1, description will be briefly made aboutthe adjacent pixel intensity difference quantization (APIDQ) histogrammethod used in the embodiment of this invention.

In FIG. 1, a face image is subjected to later-described low-passfiltering (step S1) and then adjacent pixel intensity differences arecalculated (step S2).

In step S2, a two-dimensional vector (i.e. an intensity change vectorconsisting of an intensity difference (dIx) between pixels adjacent inthe horizontal direction and an intensity difference (dIy) betweenpixels adjacent in the vertical direction) is calculated at each pixelposition in the input image. The two-dimensional vector (intensitychange vector consisting of dIx and dIy) at each pixel position in theinput image includes information about an intensity change angle (θ) andits amount (r).

The intensity change vectors calculated for all the pixels in the imageare plotted in the r-θ plane (step S3 (coordinate transformation to aθ-r system)).

Thereafter, each vector is quantized with respect to its θ value and rvalue (step S4). By counting the number of elements included in each ofregions quantized in the r-θ plane, a histogram can be generated (stepS5). The histogram obtained by the APIDQ of the face image is used asvery effective individual features.

By combining the appropriate low-pass filtering as pre-processing of aface image and the APIDQ, it is possible to extract useful features forface recognition.

The test result shows a recognition rate of 95.7% with respect to 400images of 40 individuals (10 images per individual) from the publiclyavailable AT&T (American Telephone and Telegraph Company) face database.By the use of a table lookup (TLU) method in the quantization step, thetotal recognition processing time is only 31 msec, which enables facerecognition at video rate.

Now, description will be made in detail about each of the steps of theadjacent pixel intensity difference quantization (APIDQ) histogrammethod of FIG. 1.

The adjacent pixel intensity difference quantization (APIDQ) histogrammethod shown in FIG. 1 is quite similar to the VQ histogram methodexcept the feature extraction procedure. The former (VQ histogram)method makes use of the VQ processing therefore. The VQ histogram methoduses a very basic codebook composed of 33 regular code vectors andapplies the VQ processing to an intensity change image block where theDC component was removed. This is the essence of the VQ histogram methodand the processing is merely to detect a direction and amount of anintensity change in the block and quantize them. The APIDQ can performsimilar processing more simply.

About step S2 in FIG. 1:

As shown in FIG. 2, in the APIDQ, an intensity difference (dIx) betweenpixels adjacent in the horizontal direction and an intensity difference(dIy) between pixels adjacent in the vertical direction are firstcalculated with respect to each pixel of an input face image by the useof the following simple subtraction operation.dIx(i, j)=I(i+1, j)−I(i, j)dIy(i, j)=I(i, j+1)−I(i, j)

About step S3 in FIG. 1:

The calculated pair of dIx, dIy represent a single vector (intensitychange vector) having an initial point (0, 0) in the dIx-dIy plane.After all the pixels in the input image have been processed, terminalpoints of the intensity change vectors are distributed in the dIx-dIyplane as shown in FIG. 3. The distribution (density and shape) of theterminal points of the intensity change vectors represent features ofthe input face image. By transforming the coordinate system fromorthogonal coordinates to polar coordinates, an angle θ and a distance rof each vector represent a direction and an amount of an intensitychange, respectively.

About step S4 in FIG. 1:

Then, each intensity change vector is quantized in the r-θ plane. Oneexample of a quantization table is shown in the lower part of FIG. 4.Numbers 0 to 49 in the quantization table represent index numbers of the0th to 49th quantization regions, respectively.

For example, θ of an intensity change vector shown in the upper part ofFIG. 4 is located in a region between 3π/8 and π/8 and r of thisintensity change vector is located in a third region from the inner side(r corresponds to a region between 2 and 4 in the quantization table inthe lower part of FIG. 4). Therefore, this intensity change vector shownin the upper part of FIG. 4 is quantized as a quantization region ofindex number 10 on the basis of the quantization table in the lower partof FIG. 4.

About step S5 in FIG. 1:

The number of vectors quantized into each quantization region iscounted. The counted number is indicated in a bar shape as frequency ina histogram (illustrated as FIG. 6 later) which is generated as havingthe axis of abscissas representing the index numbers 0 to 49 of thequantization regions. For example, the vector shown in the upper part ofFIG. 4 forms a part of the frequency of the index number 10 in thehistogram. This histogram serves as a feature vector of a human face.

About step S6 in FIG. 1:

In registration step S6, this histogram is stored in a database 10 asindividual identification information.

About steps S7 and S8 in FIG. 1:

In recognition step S7, a histogram is generated from an unknown inputface image and compared with registered individual histograms and thebest matched one is output as a recognition result of database matchingS8. The Manhattan distance (MD) between histograms is used as oneexample showing the degree of matching.

Referring back to step S1 in FIG. 1, attention is paid to the fact thatthe low-pass filtering is first carried out before the APIDQ by the useof a simple two-dimensional moving average filter. This low-passfiltering is essential for reducing high-frequency noise and extractinglow-frequency components that are most effective for recognition.

Since the recognition algorithm is very simple and the developed facefeature extraction method is totally different from the conventionalrecognition methods, combining it with the conventional methods is easyin a manner to enhance the recognition accuracy with the minimumadditional cost and, further, is very effective, not only using italone.

Now, description will be made about the results of a face recognitiontest using this invention.

The publicly available AT&T face database was used for the recognitiontest. The database includes 400 images in total of 40 individuals eachhaving 10 face images with differences given by face angles, faceexpressions, and lighting conditions. Each image has a resolution of92×112. FIG. 5 shows typical image samples of the AT&T database. Amongthe ten images of each individual, the five images were selected asinput images for evaluation while the remaining five were registered asregistration images in database. The recognition test was conducted withrespect to 252 (=₁₀C₅) times combinations of the input image forevaluation and the registration image in database by the use of therotation method.

Now, description will be made about the recognition performanceaccording to this invention.

FIG. 6 shows typical examples of histograms. The histograms of differentindividuals clearly differ from each other. However, the histograms ofdifferent images of the same individual resemble each other in manycases although there are small changes in details. It can be said thatthe histogram obtained by the APIDQ represents very effective individualfeatures for recognizing a person.

The recognition results are shown in FIG. 7. In FIG. 7, the recognitionrate is shown as a function of filter size. The filter size represents asize of an averaging filter core. The size of F3 represents, forexample, a size of a 3×3 filter core. “Max”, “Min”, and “Ave” representthe best result, the worst result, and the average result, respectively,with respect to the 252 (=₁₀C₅) times combinations of the input imagefor evaluation and the registration image in database. The recognitionrate is almost constant with respect to the filter sizes from F3 to F19and the highest average recognition rate of 95.7% is obtained. This isalmost equal to the case of the VQ histogram method (95.6%) under thesame conditions. Detailed face features influenced by, for example,wrinkles, a local hairstyle, the image acquisition conditions, or thelapse of time, which reduce the recognition performance, are removed bythe application of the low-pass filter. Only the important face featuresof each individual such as the rough shape of a face are extracted.Further, the APIDQ processing can effectively remove DC components ofthe pixel intensity that change according to the lighting conditions. Bythe combination of these two effects, it is possible to effectivelyextract the most important information for the face recognition.

As described above, the low-pass filtering is very effective for theface feature extraction using the APIDQ. It is possible to expect thatdifferent features can be extracted by the use of filters havingdifferent sizes. Therefore, more potent individual feature informationcan be acquired by combining multiple recognition results obtained byusing multiple filter sizes. Actually, the recognition results(similarity scores) by different-size filters were first separatelyobtained and then combined by averaging. FIG. 8 shows the recognitionresults obtained by the use of multiple filters. Herein, F3, F5, F17,and F23 represent filter sizes of 3×3, 5×5, 17×17, and 23×23,respectively. By the use of multiple filters, the recognition rateincreases by 2% and the average recognition rate becomes about 98%.

Now, description will be made about the recognition speed in thisinvention.

The recognition algorithm was programmed by the use of ANSIC andexecuted in a PC (AMD Athron 1.1 GHz). Quantization in the r-θcoordinates is implemented through simple conditional branches (“if”statements). The processing time for one image in the AT&T database was37 msec (15 msec for low-pass filtering, 7 msec for APIDQ processing,and 15 msec for database matching). As compared with the VQ processingtime in the VQ histogram method, the processing time for face featureextraction performed by the APIDQ was reduced from 164 msec (VQ) to 7msec (APIDQ). The processing time was largely shortened. Further, sincethe simple conditional branches (“if” statements) used in thequantization is not so effective for calculation speed, an attempt wasmade to use the table lookup (TLU) method instead of the “if”statements. When the TLU was applied to the r-θ domain, the APIDQprocessing time was reduced from 7 msec to 5 msec. Further, the TLU isdirectly applicable to the dIx-dIy domain. In this case, the APIDQ canbe implemented within 1 msec so that the total recognition time becomes31 msec. Although the face detection processing step is not included,the face recognition at video rate is enabled.

As described above, this invention can provide the very fast and highlyreliable face recognition method called the APIDQ histogram method. Thisface recognition method is based on the proper filtering, thequantization of the intensity change directions and amounts, and thehistogram generation and analysis. The excellent face recognitionperformance with the high recognition rate of 95.7% was confirmed by theuse of the publicly available AT&T face database.

By directly applying the table lookup (TLU) method to the dIx-dIydomain, the total recognition processing time is only 31 msec, thusenabling the face recognition at video rate.

To summarize FIG. 1, it can be considered that the data recognitiondevice according to the embodiment of this invention comprises thefollowing data analysis device 100.

That is, the data analysis device 100 comprises

difference calculation means (S2 in FIG. 1) for, with respect to animage, carrying out a calculation of calculating a difference betweenintensity values at an arbitrary point in the image and a point locatedin the vicinity of the arbitrary point in a first direction as a firstintensity difference (dIx) of the arbitrary point and of calculating adifference between intensity values at the arbitrary point and a pointlocated in the vicinity of the arbitrary point in a second directiondifferent from the first direction (e.g. perpendicular to the firstdirection) as a second intensity difference (dIy) of the arbitrarypoint, the difference calculation means carrying out the calculationwith respect to each of a plurality of points in the image, and

frequency distribution generation means (S3 to S5 in FIG. 1) forallocating a vector (intensity change vector) comprising the firstintensity difference and the second intensity difference obtained by thedifference calculation means for each of the plurality of points in theimage to a single region of a plurality of regions (regions representedby the index numbers 0 to 49 of the quantization table in FIG. 4)divided by a predetermined region division method and generating afrequency distribution of the plurality of regions by using the numberof vectors allocated to each of the plurality of regions as frequency ofthe corresponding region.

Herein, the predetermined region division method is not limited to themethod, used in the foregoing embodiment, that allocates an intensitychange vector to a single region of a plurality of regions in the r-θplane by coordinate transformation to the θ-r system, and other regiondivision methods may be used as the predetermined region divisionmethod.

Further, the difference calculation means may, with respect to the imagedata, carry out a calculation of calculating an intensity difference dIxof an arbitrary point in an x-direction in an image as a differencebetween an intensity value at the arbitrary point and an intensity valueat a point on a right side (or a point on a left side) of the arbitrarypoint and of calculating an intensity difference dIy of the arbitrarypoint in a y-direction as a difference between the intensity value atthe arbitrary point and an intensity value at a point on a lower side(or a point on an upper side) of the arbitrary point, the differencecalculation means carrying out the calculation with respect to each of aplurality of points in the image.

In the data analysis device 100, feature data may be produced byextracting a frequency distribution of at least a part of the pluralityof regions from the frequency distribution of the plurality of regionsgenerated by the frequency distribution generation means.

It can be considered that the data recognition device according to theembodiment of this invention comprises the following means in additionto the data analysis device 100.

That is, the data recognition device comprises

frequency distribution storage means (database 10 in FIG. 1) for storinginformation of one or more frequency distributions with respect to atleast one or more images, and

comparison means (S8 in FIG. 1) for comparing between one or morefrequency distributions about the image generated by the frequencydistribution generation means and the one or more frequencydistributions in the frequency distribution storage means.

Preferably, the comparison means compares between the one or morefrequency distributions about the image generated by the frequencydistribution generation means and the frequency distributions in thefrequency distribution storage means and selects the frequencydistribution specified by a predetermined comparison function from therespective frequency distributions stored in the frequency distributionstorage means.

In this data recognition device, the one or more frequency distributionsabout the image generated by the frequency distribution generation meansmay be one or more frequency distributions of a part of the plurality ofregions extracted from the frequency distribution of the plurality ofregions generated by the frequency distribution generation means.

The data recognition device may comprise frequency distributionregistration means (S6 in FIG. 1) for registering the frequencydistribution generated by the frequency distribution generation meansinto the frequency distribution storage means when, as a result of thecomparison by the comparison means (S8 in FIG. 1), the frequencydistribution to be selected by the predetermined comparison functiondoes not exist in the frequency distribution storage means.

The data recognition device may further comprise filter means (S1 inFIG. 1) for applying filtering to input image data and the differencecalculation means may carry out the difference calculation with respectto the image data applied with the filtering by the filter means.

The filter means (S1 in FIG. 1) is not limited to the low-pass filterused in the foregoing embodiment and other filters may be used as thisfilter means.

Alternatively, a plurality of filter means may be provided for applyinga plurality of filtering processes to the input image data and thedifference calculation means may carry out the difference calculationwith respect to the image data applied with the filtering processes bythe plurality of filter means.

In this case, the data recognition device may comprise frequencydistribution storage means for storing, in a plurality of sets,information of the frequency distributions of the plurality of regions,and comparison means for comparing between the frequency distributionabout the input image data generated by the frequency distributiongeneration means and the respective sets of the frequency distributionsin the frequency distribution storage means and selecting one set of thefrequency distributions specified by a predetermined comparison functionfrom the respective sets of the frequency distributions stored in thefrequency distribution storage means.

This invention is not limited to the application to the face recognitiondescribed in the foregoing embodiment, but may naturally be applied tohigh-speed data recognition of general images or other large volumedata.

As described above, according to this invention, there are obtained adata analysis device that achieves high processing speed and a datarecognition device using such a data analysis device, thereby enablinghigh-speed data recognition or instantaneous data recognition.

1. A data analysis device characterized by comprising: differencecalculation means for, with respect to an image, carrying out acalculation of calculating a difference between intensity values at anarbitrary point in said image and a point located in the vicinity ofsaid arbitrary point in a first direction as a first intensitydifference of said arbitrary point and of calculating a differencebetween intensity values at said arbitrary point and a point located inthe vicinity of said arbitrary point in a second direction differentfrom said first direction as a second intensity difference of saidarbitrary point, said difference calculation means carrying out saidcalculation with respect to each of a plurality of points in said image,and frequency distribution generation means for allocating a vectorcomprising said first intensity difference and said second intensitydifference obtained by said difference calculation means for each of theplurality of points in said image to a single region of a plurality ofregions divided by a predetermined region division method and generatinga frequency distribution of said plurality of regions by using thenumber of vectors allocated to each of said plurality of regions asfrequency of the corresponding region.
 2. A data analysis deviceaccording to claim 1, characterized by producing feature data byextracting a frequency distribution of at least a part of said pluralityof regions from the frequency distribution of said plurality of regionsgenerated by said frequency distribution generation means.
 3. A dataanalysis device according to claim 1, characterized by applyingfiltering to said image before said difference calculation means carriesout said calculation with respect to said image.
 4. A data recognitiondevice characterized by comprising frequency distribution storage meansfor storing information of one or more frequency distributions withrespect to at least one or more images, difference calculation meansfor, with respect to an image, carrying out a calculation of calculatinga difference between intensity values at an arbitrary point in saidimage and a point located in the vicinity of said arbitrary point in afirst direction as a first intensity difference of said arbitrary pointand of calculating a difference between intensity values at saidarbitrary point and a point located in the vicinity of said arbitrarypoint in a second direction different from said first direction as asecond intensity difference of said arbitrary point, said differencecalculation means carrying out said calculation with respect to each ofa plurality of points in said image, frequency distribution generationmeans for allocating a vector comprising said first intensity differenceand said second intensity difference obtained by said differencecalculation means for each of the plurality of points in said image to asingle region of a plurality of regions divided by a predeterminedregion division method and generating a frequency distribution of saidplurality of regions by using the number of vectors allocated to each ofsaid plurality of regions as frequency of the corresponding region, andcomparison means for comparing between one or more frequencydistributions about said image generated by said frequency distributiongeneration means and said one or more frequency distributions in saidfrequency distribution storage means.
 5. A data recognition deviceaccording to claim 4, characterized in that said comparison meanscompares between said one or more frequency distributions about saidimage generated by said frequency distribution generation means and saidfrequency distributions in said frequency distribution storage means andselects the frequency distribution specified by a predeterminedcomparison function from the respective frequency distributions storedin said frequency distribution storage means.
 6. A data recognitiondevice according to claim 5, characterized in that said one or morefrequency distributions about said image generated by said frequencydistribution generation means are one or more frequency distributions ofa part of said plurality of regions extracted from the frequencydistribution of said plurality of regions generated by said frequencydistribution generation means.
 7. A data recognition device according toclaim 4, characterized by applying filtering to said image before saiddifference calculation means performs said calculation with respect tosaid image.
 8. A data recognition device characterized by comprisingfilter means for applying filtering to input image data, differencecalculation means for, with respect to the image data applied with thefiltering by said filter means, carrying out a calculation ofcalculating an intensity difference dIx of an arbitrary point in anx-direction in an image as a difference between an intensity value atsaid arbitrary point and an intensity value at a point on a right orleft side of said arbitrary point and of calculating an intensitydifference dIy of said arbitrary point in a y-direction as a differencebetween the intensity value at said arbitrary point and an intensityvalue at a point on a lower or upper side of said arbitrary point, saiddifference calculation means carrying out said calculation with respectto each of a plurality of points in said image, frequency distributiongeneration means for allocating a vector comprising said intensitydifference in the x-direction and said intensity difference in they-direction obtained by said difference calculation means for each ofthe plurality of points in said image to a single region of a pluralityof regions divided by a predetermined region division method andgenerating a frequency distribution of said plurality of regions byusing the number of vectors allocated to each of said plurality ofregions as frequency of the corresponding region, frequency distributionstorage means for storing information of at least one or more frequencydistributions, and comparison means for comparing between the frequencydistribution about said input image data generated by said frequencydistribution generation means and said frequency distributions in saidfrequency distribution storage means and selecting the frequencydistribution specified by a predetermined comparison function from therespective frequency distributions stored in said frequency distributionstorage means.
 9. A data recognition device characterized by comprisinga plurality of filter means for applying a plurality of filteringprocesses to input image data, difference calculation means for, withrespect to the image data applied with the filtering processes by saidplurality of filter means, carrying out a calculation of calculating anintensity difference dIx of an arbitrary point in an x-direction in animage as a difference between an intensity value at said arbitrary pointand an intensity value at a point on a right or left side of saidarbitrary point and of calculating an intensity difference dIy of saidarbitrary point in a y-direction as a difference between the intensityvalue at said arbitrary point and an intensity value at a point on alower or upper side of said arbitrary point, said difference calculationmeans carrying out said calculation with respect to each of a pluralityof points in said image, frequency distribution generation means forallocating a vector comprising said intensity difference in thex-direction and said intensity difference in the y-direction obtained bysaid difference calculation means for each of the plurality of points insaid image to a single region of a plurality of regions divided by apredetermined region division method and generating a frequencydistribution of said plurality of regions by using the number of vectorsallocated to each of said plurality of regions as frequency of thecorresponding region, frequency distribution storage means for storing,in a plurality of sets, information of the frequency distributions ofsaid plurality of regions, and comparison means for comparing betweenthe frequency distribution about said input image data generated by saidfrequency distribution generation means and the respective sets of thefrequency distributions in said frequency distribution storage means andselecting one set of the frequency distributions specified by apredetermined comparison function from the respective sets of thefrequency distributions stored in said frequency distribution storagemeans.
 10. A data recognition device according to claim 5, characterizedby comprising frequency distribution registration means for registeringthe frequency distribution generated by said frequency distributiongeneration means into said frequency distribution storage means when, asa result of the comparison by said comparison means, the frequencydistribution to be selected by said predetermined comparison functiondoes not exist in said frequency distribution storage means.
 11. A dataanalysis device according to claim 2, characterized by applyingfiltering to said image before said difference calculation means carriesout said calculation with respect to said image.
 12. A data recognitiondevice according to claim 5, characterized by applying filtering to saidimage before said difference calculation means performs said calculationwith respect to said image.
 13. A data recognition device according toclaim 6, characterized by applying filtering to said image before saiddifference calculation means performs said calculation with respect tosaid image.
 14. A data recognition device according to claim 6,characterized by comprising frequency distribution registration meansfor registering the frequency distribution generated by said frequencydistribution generation means into said frequency distribution storagemeans when, as a result of the comparison by said comparison means, thefrequency distribution to be selected by said predetermined comparisonfunction does not exist in said frequency distribution storage means.15. A data recognition device according to claim 8, characterized bycomprising frequency distribution registration means for registering thefrequency distribution generated by said frequency distributiongeneration means into said frequency distribution storage means when, asa result of the comparison by said comparison means, the frequencydistribution to be selected by said predetermined comparison functiondoes not exist in said frequency distribution storage means.
 16. A datarecognition device according to claim 9, characterized by comprisingfrequency distribution registration means for registering the frequencydistribution generated by said frequency distribution generation meansinto said frequency distribution storage means when, as a result of thecomparison by said comparison means, the frequency distribution to beselected by said predetermined comparison function does not exist insaid frequency distribution storage means.